When people purchase a product from an online store, they are usually provided with product-related information such as product description, product images, and user reviews. Often, product specification is also provided to specify its features in an organized way, especially for high-technology products that consist of several electronic components, and it is highly informative for users to understand the product. An example of a digital camera product specification is shown in FIG. 1.
However, it is often hard to understand what the contents of product specification really mean when the consumers are unfamiliar with them. For example, when novice consumers read a digital camera's specification, they would not have any idea what the value “TTL phase detection” of feature “Auto Focus” means because they are not familiar with it. Not only such consumers are strange to what the feature value is, but also they do not know what it really means to them.
In order to choose “right” value of a feature, consumers would like to hear direct experience from consumers who own a product equipped with it, which may answer questions such as “is the feature value preferred by others?” A typical product purchase from online stores is depicted in FIG. 2. When a customer clicks a link for a certain product, the customer reads product-related information such as product description, reviews, and specifications. If the customer purchases the product, he or she uses the product for a while and possibly leaves a review to share experience with other people. The consumers can learn what the feature value is through Wikipedia or Web search, but it is laborious to find what other consumers say about a certain feature value from reviews of a product with the feature. Moreover, if the product does not have enough information about the feature, one needs to collect such information from reviews of other products, which is time-consuming.
Opinion mining and summarization have been widely studied. Most of the studies performed research on product review or Weblog data set since people leave rich opinions on them. In order to know the target of opinions and to mine opinions in a more effective way, aspect-based opinion mining and summarization has been studied as a main stream in the field. To find aspects of a product, many studies applied a topic model, which find latent topics from documents. Most existing works in this line of research mine opinions for a product feature, either pre-defined or latent.
Although product specifications have been available in many e-commerce sites, only a limited number of studies used them for product review analysis. For example, Ontology-Supported Polarity Mining (OSPM), which takes advantage of domain ontology database from IMDb, aims to achieve sentiment classification on reviews. However, the method studied only movie properties (features), not feature values. Other methods employ product review analysis, but the goal is document categorization. Product specifications and reviews are also used to build an aspect hierarchy, but the method did not study feature values. Other studies used product specifications to summarize product features, but they also did not study feature values.
Therefore, most topic model-based opinion mining and summarization techniques do not use pre-defined topics (e.g. product specifications) for product review analysis. Further, those opinion mining techniques that incorporates product specifications still fail to address the problem that novice consumers have little knowledge of the actual value corresponding to a feature in product specifications.
The disclosed method and system are directed to solve one or more problems set forth above and other problems.